2021 10th International IEEE/EMBS Conference on Neural Engineering (NER)最新文献_第3页

2021 10th International IEEE/EMBS Conference on Neural Engineering (NER) Pub Date : 2021-05-04 DOI: 10.1109/NER49283.2021.9441370

Elena Manferlotti, M. Vissani, A. Mazzoni, Arvind Kumar

{"title":"Correlated inputs to striatal population drive subthalamic nucleus hyper-synchronization","authors":"Elena Manferlotti, M. Vissani, A. Mazzoni, Arvind Kumar","doi":"10.1109/NER49283.2021.9441370","DOIUrl":"https://doi.org/10.1109/NER49283.2021.9441370","url":null,"abstract":"Movement disorders such as Parkinson's disease (PD) are often associated with hyper-synchronization in basal ganglia (BG) activity. The origins of the hyper-synchronization and its interaction with deep brain stimulations (DBS) of BG nuclei are still debated. The structure of the BG suggests that strong neuronal correlations cannot arise within the BG but should rather be inherited from cortical or thalamic inputs. However, this is inconsistent with the hypothesized decorrelating role of the striatal network. Here we investigate to what extent striatum can decorrelate correlated inputs, and the consequences of correlations in striatal activity on the dynamics of the BG; especially, the beta-band oscillations, which are the cardinal symptom of PD. We developed a BG network model with spiking neurons and analyzed the effects on input correlation on BG activity. We found that striatum does not completely decorrelate the inputs and that the residual correlations can synchronize the whole BG. These results provide new insights into the origin of BG hyper-synchronization and pave the way to understand DBS induced disruption of synchrony and ensuing therapeutical effects.","PeriodicalId":413977,"journal":{"name":"2021 10th International IEEE/EMBS Conference on Neural Engineering (NER)","volume":"104 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-05-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124861918","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 2

Quantifying Neural Processing Changes During an Auditory Oddball Task Under Hypoxia Using Electroencephalography 用脑电图量化缺氧条件下听觉怪球任务中的神经加工变化

2021 10th International IEEE/EMBS Conference on Neural Engineering (NER) Pub Date : 2021-05-04 DOI: 10.1109/NER49283.2021.9441116

S. Yuditskaya, J. Williamson, G. Ciccarelli, Kara J. Blacker, M. Funke, B. Telfer

{"title":"Quantifying Neural Processing Changes During an Auditory Oddball Task Under Hypoxia Using Electroencephalography","authors":"S. Yuditskaya, J. Williamson, G. Ciccarelli, Kara J. Blacker, M. Funke, B. Telfer","doi":"10.1109/NER49283.2021.9441116","DOIUrl":"https://doi.org/10.1109/NER49283.2021.9441116","url":null,"abstract":"To investigate neural phenotypes specific to hypoxia’ we quantify the capability of electroencephalography (EEG) dynamics to determine if an individual is in a normoxic or hypoxic state during a passive auditory task and identify EEG-based features that provide maximal discriminability. Two distinct modeling approaches are explored, each intended to capture contrasting aspects of the relationship between neural activity and the hypoxic physiological state. With an analysis that exploits Auditory Deviance Response landmarks to characterize cognitive-neurophysiological changes during hypoxia, we achieve a receiver operating characteristic area under the curve (AUC) of 0.76 using only two fronto-temporal channels, on par with larger EEG channel set benchmarks. With a novel EEG correlation structure approach that examines finer neural coordination patterns across a spatially diverse channel set, we achieve an AUC of 0.85. Our methods of EEG analysis may find applications in understanding and detecting environmental impact on neural health.","PeriodicalId":413977,"journal":{"name":"2021 10th International IEEE/EMBS Conference on Neural Engineering (NER)","volume":"12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-05-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132097289","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Phase-Amplitude Coupling between Neuronal Wideband Low-Frequency Oscillations and Broadband Gamma Activity 神经元宽带低频振荡与宽带伽马活动的相幅耦合

2021 10th International IEEE/EMBS Conference on Neural Engineering (NER) Pub Date : 2021-05-04 DOI: 10.1109/NER49283.2021.9441250

Tao Xie, Zehan Wu, Liang Chen, Xiangyang Zhu, X. Sheng, P. Brunner

引用次数: 0

A programmable, multichannel, miniature stimulator for electrotactile feedback of neural hand prostheses 一种可编程、多通道、用于神经假肢电触觉反馈的微型刺激器

2021 10th International IEEE/EMBS Conference on Neural Engineering (NER) Pub Date : 2021-05-04 DOI: 10.1109/NER49283.2021.9441192

Han Wang, G. Chai, X. Sheng, Xiangyang Zhu

引用次数: 1

Technical Note: A Low-Cost Research Platform for Brain-Computer-Interface Applications in Mixed Reality 技术说明:混合现实中脑机接口应用的低成本研究平台

2021 10th International IEEE/EMBS Conference on Neural Engineering (NER) Pub Date : 2021-05-04 DOI: 10.1109/NER49283.2021.9441260

Griffin W. Milsap, Preston Peranich

{"title":"Technical Note: A Low-Cost Research Platform for Brain-Computer-Interface Applications in Mixed Reality","authors":"Griffin W. Milsap, Preston Peranich","doi":"10.1109/NER49283.2021.9441260","DOIUrl":"https://doi.org/10.1109/NER49283.2021.9441260","url":null,"abstract":"Many brain-computer-interface (BCI) approaches, such as the steady-state-visually-evoked-potential (SSVEP) and BCI speller paradigms, rely on evoking neural signals using presentation of audiovisual stimuli. These exogenous BCIs typically involve the user staring at a physical stimulus presentation apparatus resulting in prolonged system setup and degraded portability. We present a research platform for BCI implementations in mixed reality (XR) environments, enabling less-invasive holographic stimulus presentation and greatly enhanced portability at low-cost. The platform consists of a 3D-printable electroencephalography (EEG) attachment for the Microsoft HoloLens, firmware for the OpenBCI Cyton EEG amplifier evaluation kit, and Unity software supporting direct Bluetooth low-energy communication between the EEG device and the HoloLens. The platform allows researchers and hobbyists to prototype BCIs with an easy to don/doff EEG attachment for the HoloLens that is capable of recording high-quality EEG using dry electrodes in an untethered XR environment without the need for external compute resources.","PeriodicalId":413977,"journal":{"name":"2021 10th International IEEE/EMBS Conference on Neural Engineering (NER)","volume":"27 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-05-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122198558","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

EEG-based Classification of the Intensity of Emotional Responses 基于脑电图的情绪反应强度分类

2021 10th International IEEE/EMBS Conference on Neural Engineering (NER) Pub Date : 2021-05-04 DOI: 10.1109/NER49283.2021.9441371

Vahan Babushkin, Wanjoo Park, Muhammad Hassan Jamil, Haneen Alsuradi, M. Eid

{"title":"EEG-based Classification of the Intensity of Emotional Responses","authors":"Vahan Babushkin, Wanjoo Park, Muhammad Hassan Jamil, Haneen Alsuradi, M. Eid","doi":"10.1109/NER49283.2021.9441371","DOIUrl":"https://doi.org/10.1109/NER49283.2021.9441371","url":null,"abstract":"Considering the fact that emotional experiences are stored in the brain, classifying emotion from brain activity measured using electroencephalography has become a trend. In most of the previous studies, the user's emotions were classified based on a stimulus. In this paper, we present a model that can classify the emotion intensity by the participants' self-report. Two machine learning classifiers are considered: support vector machine (SVM) and convolutional neural networks (CNN). Results demonstrated that both SVM and CNN models perform well with four classes of emotions (positive/negative valence high/low arousal combination) where SVM achieved an accuracy of 85% whereas CNN achieved 81%. Considering 12 classes of emotional responses (low, medium, and high intensity for positive/negative valence high/low arousal combination) by the participants' self report resulted in an accuracy of 70% for SVM and 69% for CNN. The proposed model excels in classifying emotional intensity and provides superior performance compared to the state-of-the-art emotion classification systems.","PeriodicalId":413977,"journal":{"name":"2021 10th International IEEE/EMBS Conference on Neural Engineering (NER)","volume":"439 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-05-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116639685","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

A Cortical Extracellular Simulation Model to Create Synthetic Neural Recordings 皮质细胞外模拟模型创建合成神经记录

2021 10th International IEEE/EMBS Conference on Neural Engineering (NER) Pub Date : 2021-05-04 DOI: 10.1109/NER49283.2021.9441104

Kyle Gherardi, H. Töreyin

引用次数: 1

Population Activity in Motor Cortex is Influenced by the Contexts of the Motor Behavior 运动皮层的群体活动受运动行为环境的影响

2021 10th International IEEE/EMBS Conference on Neural Engineering (NER) Pub Date : 2021-05-04 DOI: 10.1109/NER49283.2021.9441430

Xuan Ma, Kevin L. Bodkin, L. Miller

{"title":"Population Activity in Motor Cortex is Influenced by the Contexts of the Motor Behavior","authors":"Xuan Ma, Kevin L. Bodkin, L. Miller","doi":"10.1109/NER49283.2021.9441430","DOIUrl":"https://doi.org/10.1109/NER49283.2021.9441430","url":null,"abstract":"Most sensorimotor studies investigating the covariation of populations of neurons in primary motor cortex (M1) have considered only a few trained movements made under highly constrained conditions. However, motor behaviors in daily living happen in a far more complex and varied contexts. It is unclear whether M1 neurons would have different population responses in a more naturalistic, unconstrained setting, including requirements to accommodate multiple limbs and body posture, and more extensive proprioceptive inputs. Here, we recorded M1 spiking signals while a monkey performed hand grasp movements in two different contexts: one in the typical constrained lab setting, and the other while moving freely in a large plastic cage. We compared the covariance patterns of the neural activity during movements across the two contexts. We found that the neural covariation patterns accompanying two different hand grasps in the unconstrained context were largely preserved, while they differed across contexts, even for the same type of grasp. We also found that the M1 population activity was confined to context-dependent neural manifolds, but these manifolds were not completely independent, as some dimensions appeared to be shared across the contexts. These results suggest that the coordinated activity of M1 neurons is strongly dependent on behavioral context, in ways that were not entirely anticipated.","PeriodicalId":413977,"journal":{"name":"2021 10th International IEEE/EMBS Conference on Neural Engineering (NER)","volume":"41 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-05-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132568112","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 2

An Adversarial Variational Autoencoder Approach Toward Transfer Learning for mTBI Identification 一种对抗变分自编码器方法用于mTBI识别的迁移学习

2021 10th International IEEE/EMBS Conference on Neural Engineering (NER) Pub Date : 2021-05-04 DOI: 10.1109/NER49283.2021.9441372

Shiva Salsabilian, L. Najafizadeh

{"title":"An Adversarial Variational Autoencoder Approach Toward Transfer Learning for mTBI Identification","authors":"Shiva Salsabilian, L. Najafizadeh","doi":"10.1109/NER49283.2021.9441372","DOIUrl":"https://doi.org/10.1109/NER49283.2021.9441372","url":null,"abstract":"Subject variability in mild traumatic brain injury (mTBI) data has often been an obstacle in accurate injury-related feature extraction and biomarker identification for successful mTBI diagnosis. To address this issue, we propose an adversarial variational autoencoder model as a novel regularization approach to extract subject-invariant representations for transfer learning in mTBI identification. The proposed method consists of a variational autoencoder with an attached adversarial network. The autoencoder attempts to learn the latent space mappings from neural activity, while the adversary network is used in a discriminative setting to detach the subject individuality from the representations. The trained encoder is then transferred to extract the representations from new subject's data. Several classifiers are utilized to classify the extracted representations into two categories of normal and mTBI. To evaluate the performance of the proposed method, recorded cortical activity of GCaMP6s transgenic calcium reporter mice before and after inducing an injury is used. Experimental results on cross-subject transfer learning exhibit the efficiency of the proposed framework by achieving 89.7% classification accuracy, suggesting the feasibility of the proposed method in learning invariant representations in mTBI data.","PeriodicalId":413977,"journal":{"name":"2021 10th International IEEE/EMBS Conference on Neural Engineering (NER)","volume":"30 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-05-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133226609","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 3

Advanced Artifact Removal for Automated TMS-EEG Data Processing 自动TMS-EEG数据处理的高级伪影去除

2021 10th International IEEE/EMBS Conference on Neural Engineering (NER) Pub Date : 2021-05-04 DOI: 10.1109/NER49283.2021.9441147

Christopher C. Cline, Molly V. Lucas, Yinming Sun, Matthew Menezes, A. Etkin

引用次数: 5